{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chap2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:53:10.359415Z",
     "start_time": "2019-03-28T03:53:09.965Z"
    }
   },
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "# set the path\n",
    "setwd(getwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig2-6[Histogram]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:53:10.451275Z",
     "start_time": "2019-03-28T03:53:09.972Z"
    }
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "# 仅有一列数据，选取此列,从data.frame转化为vector\n",
    "loans_income <- read.csv(\"../psds_data/loans_income.csv\")[, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:53:10.470797Z",
     "start_time": "2019-03-28T03:53:09.979Z"
    }
   },
   "outputs": [],
   "source": [
    "# take a somple random sample\n",
    "samp_data <- data.frame(income=sample(loans_income, 1000),\n",
    "                       type = 'data_dist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:53:10.512759Z",
     "start_time": "2019-03-28T03:53:09.983Z"
    }
   },
   "outputs": [],
   "source": [
    "# take s sample of means of 5 values\n",
    "sample_mean_05 <- data.frame(\n",
    "    income = tapply(sample(loans_income, 1000*5), rep(1:1000, rep(5, 1000)),FUN=mean),\n",
    "    type = 'mean_of_5'\n",
    ")\n",
    "\n",
    "# take s sample of means of 20 values\n",
    "sample_mean_20 <- data.frame(\n",
    "    income = tapply(sample(loans_income, 1000*20), rep(1:1000, rep(20, 1000)),FUN=mean),\n",
    "    type = 'mean_of_20'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:53:10.544774Z",
     "start_time": "2019-03-28T03:53:09.992Z"
    }
   },
   "outputs": [],
   "source": [
    "# bind the data.frames and convert type to a factor\n",
    "income <- rbind(samp_data, sample_mean_05, sample_mean_20)\n",
    "income$type <- factor(\n",
    "    income$type, \n",
    "    levels = c('data_dist', 'mean_of_5', 'mean_of_20'),\n",
    "    labels = c('Data', 'Mean of 5', 'Mean of 20')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:55:00.859706Z",
     "start_time": "2019-03-28T03:54:59.634Z"
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fu6qPTKC2xIem0wJL20j/VLrx3sk14a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\nCxL0jIAF31oAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the histogram\n",
    "options(repr.plot.width=5, repr.plot.height=5)  # plot size\n",
    "ggplot(income, aes(x=income))+\n",
    "    geom_histogram(bins = 40)+\n",
    "    facet_grid(type~.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig2-11[QQ-Plot]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T03:59:50.863094Z",
     "start_time": "2019-03-28T03:59:50.739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HvgV7VcVjsdhTZc+YCVuT3sMglyFoUUsaIVecOwdzhvdoml1iZ7FusXHy2owQV0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\nRK5CYII=",
      "text/plain": [
       "Plot with title “Normal Q-Q Plot”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_samp <- rnorm(100)\n",
    "qqnorm(norm_samp)\n",
    "abline(a = 0, b = 1, col='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig2-12[QQ-Plot]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-28T04:02:02.269288Z",
     "start_time": "2019-03-28T04:01:49.460Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hFM7wAIC7EsQlLB8uU1M3YAQ7DDChCUJ6G/uT5W3Fj6/A/ZDqgTDmKNcM3YRe4M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\nSUVORK5CYII=",
      "text/plain": [
       "Plot with title “Normal Q-Q Plot”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500_px <- read.csv(\"../psds_data/sp500_data.csv\")\n",
    "nflx <- sp500_px[, 'NFLX']\n",
    "nflx <- diff(log(nflx[nflx>0]))\n",
    "qqnorm(nflx)\n",
    "abline(a=0, b=1, col='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R 3.4",
   "language": "R",
   "name": "ir34"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.3"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "273.2px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
